Computed tomography recent history and future perspectives

Abstract

Purpose: We provide a review of the key computed tomography (CT) technologies developed since the late 1980s and offer an overview of one of the future technologies under development. The focus of this review is mainly on the hardware and system development. The topics on the historical event linked to the early days of CT development and other innovations that contributed to the CT development, such as advanced image reconstruction techniques, are covered by companion papers in this special issue. Approach: The review is divided into five major sections, each linked to a key innovation in CT: helical spiral data acquisition, multi-slice CT, wide-cone CT, dual-source CT, and spectral CT. Given the limited scope of this review, only one of the future technologies, photon-counting CT, is discussed in detail. Whenever possible, both theory of operation and clinical examples are provided. Results: Theoretical analyses, phantom results, and clinical examples clearly demonstrate the efficacy and clinical relevancy of five historical technology developments and one future technology in CT. These technologies have improved and will continue to improve CT performance in terms of isotropic volume coverage, improved temporal resolution, and material differentiation and characterization capabilities. Conclusions: Over the past 30 years, technological developments of CT have contributed to the success of CT in many clinical applications such as trauma, oncology, cardiac imaging, and stroke. Advanced clinical applications have and will continue to demand more advanced technology development.

Keywords: dual-source; helical and spiral; multi-slice; photon-counting; spectral; wide-cone.

Fig. 1

Spiral interpolation to the plane of the black circle with algorithm 360-LI.

Fig. 2

Effective slice-width in spiral/helical CT: the collimated slice profile, indicated in red, is a trapezoid. The slice profiles after spiral/helical interpolation are bell-shaped (see the green curves for the most commonly used 180-LI approach at different pitch values).

Fig. 3

First spiral CT scan of the lung presented at RSNA 1989 (courtesy of Willi Kalender).

Fig. 4

$Z$ coverage as a function of helical pitch with a 20-s breath-hold at 1-s gantry rotation speed.

Fig. 5

Illustration of different CT geometries: (a) a single-slice CT and (b) an eight-slice CT. For the single-slice configuration, the slice thickness is defined by the prepatient collimation (beamwidth) while for multi-slice system, the slice thickness is defined by the detector row width.

Fig. 6

Illustration of cone-beam artifact with a helical body phantom. Images were acquired in a step-and-shoot mode data acquisition. (a) The center row location and image and (b) an edge row position.

Fig. 7

Clinical images acquired on a 64-slice scanner (a) coronal image of an abdomen and pelvis study and (b) volume rendered image of a carotid with circle of Willis CTA study.

Fig. 8

Impact of a narrow detector on CCTA imaging. (a) The detector covers only a fraction of the heart and multiple acquisitions are needed to enable a full coverage. (b) An example of CCTA acquired on a 16-row detector with phase misregistration (red arrows).

Fig. 9

CCTA images of an arrhythmia patient with heart rate varying from 38 to 111 bpm acquired on a 16-cm detector system in one cardiac cycle at 0.6 mSv. (a) 3D volume rendered image; (b) reformatted image of the left anterior descending artery (LAD); and (c) reformatted image of the right coronary artery (RCA). Image courtesy of Prof. Kaufmann, USZ, Zürich, Switzerland.

Fig. 10

Example of a 3-year-old pediatric patient imaged without sedation (heart rate 117 bpm). A 16-cm gated axial data acquisition at 70 kVp was used (${\mathrm{CTDI}}_{\mathrm{vol}}=1.3\mathrm{mGy}$). (a) Axial image displayed with a lung window; (b) axial image displayed with a soft-tissue window; and (c) coronal image displayed at a soft-tissue window. Image courtesy of Dr. W. Dennis Foley, Froedtert and Medical College of Wisconsin, USA.

Fig. 11

Illustration of the impact of scattered radiation and a hardware approach for scatter rejection: (a) image acquired on a 1D postpatient collimation system with red arrows indicating scatter artifacts and (b) image acquired on a 2D focusing collimation system with additional scatter rejection in $z$.

Fig. 12

Reconstructed images of a computer simulation of a chest phantom: (a) illustration of cone beam artifact (red arrows) with FDK reconstruction algorithm and (b) artifact suppression with a more advanced reconstruction algorithm.

Fig. 13

DSCT with two independent measurement systems: (a) first generation with a system angle of 90 deg and (b) second generation. To increase the SFOV of detector B to 33 cm, a larger system angle of 95 deg was chosen. With the third-generation DSCT (c), the SFOV of detector B was further increased to 35.5 cm.

Fig. 14

CCTA images of a patient with bypasses and atrial fibrillation with unstable heart rate, acquired on a third-generation DSCT. ECG-triggered sequential scan with a scan range of 21.9 cm. (a), (b) 3D volume rendered images (VRT) and (c) curved MPR. The arrow illustrates a stent. Image courtesy of Peking Union Medical College (PUMC), Beijing, China.

Fig. 15

Principle of the ECG-triggered high-pitch scanning with DSCT. The scan data for images at adjacent $z$ positions are acquired within the same cardiac cycle at slightly different cardiac phases. Figure with modifications from Ref. .

Fig. 16

ECG-triggered high-pitch spiral scan of the aorta in an emergency situation: aortic dissection with affected left renal artery; acquired with a third-generation DSCT at pitch 3.2, total scan time 0.8 s, 90 kVp, ${\mathrm{CTDI}}_{\mathrm{vol}}=2.81\mathrm{mGy}$, $\mathrm{DLP}=178\mathrm{mGycm}$. Images courtesy of Klinikum Großhadern, Ludwigs-Maximilians University Munich (LMU), Germany.

Fig. 17

Images of an anthropomorphic thorax phantom with heart insert, scanned on a DSCT system. The x-ray beamwidth in the $z$ direction was 38.4 mm at isocenter. FoV 420 mm, window width 300 HU, window center 40 HU. (a) No scatter correction. The arrows indicate scatter artifacts due to direct scatter and cross scatter. (b) Measurement-based scatter correction. (c) Model-based scatter correction. Images with modification from Ref. .

Fig. 18

Illustration of material differentiation with DECT on a Gammex phantom (window width 500 HU, window center 100 HU): (a) 140 kVp image and (b) 80 kVp image.

Fig. 19

X-ray mass attenuation coefficients as a function of x-ray photon energy: (a) water and (b) calcium.

Fig. 20

Illustration of material decomposition on a Gammex phantom: (a) water (iodine) image in mg/ml and (b) iodine (water) image in mg/ml.

Fig. 21

Illustration of DECT to improve the contrast-to-noise ratio and the lesion detectability for a patient with pancreatic cancer: (a) 40-keV virtual monochromatic image, (b) 70-keV virtual monochromatic image, (c) iodine (water) image, and (d) color-overlay image. Image courtesy of Dr. Nakul Gupta, Houston Methodist Hospital, USA.

Fig. 22

Illustration of DECT for pulmonary emboli detection (a) 70-keV image to show thrombus (b) color-overlayed image to highlight the affected lung region. Image courtesy of Dr. W. Dennis Foley, Froedtert & Medical College of Wisconsin, USA.

Fig. 23

Schematic drawing of an energy-integrating scintillator detector: (a) side view and (b) top view. The $z$ direction is the patient’s longitudinal direction. Detector cells made of a scintillator such as GOS absorb the x-rays (red arrows) and convert their energy into visible light (orange circles).

Fig. 24

Schematic drawing of a direct converting photon-counting detector: (a) side view and (b) top view. The x-rays (red arrows) absorbed in a semiconductor such as CdTe or CZT produce electron–hole pairs that are separated in a strong electric field between cathode and pixelated anodes. A potential subpixel structure is indicated for the three left detector cells. The pixelated anodes must then be correspondingly structured (not shown here in order not to overload the drawing).

Fig. 25

The signal pulses induced by absorbed x-rays in a photon-counting detector are counted as soon as they exceed a threshold $T_0$ (dashed blue line, “counting” is indicated by a blue dot). $T_0$ has a typical energy of 25 keV, well above the low-amplitude baseline noise. Three additional thresholds at higher energies ($T_1$ at 50 keV, $T_2$ at 75 keV, and $T_3$ at 90 keV) are also indicated—simultaneous read-out of the counts at various energy thresholds (in this example 4) provides spectrally resolved detector signals.

Fig. 26

Edge-on realization of a photon counting detector. This design is suitable for detector materials such as silicon, which have lower x-ray attenuation coefficients. The distribution of interactions over a larger volume potentially helps to mitigate pulse pileup effects.

Fig. 27

Contrast-enhanced kidney scan acquired with a preclinical photon counting CT prototype with four low-energy thresholds (25, 50, 75, and 90 keV) as indicated in Fig. 25, operated at an x-ray tube voltage of 140 kVp. The higher the lower energy threshold is, the lower is the iodine contrast, and the higher is the image noise in the reconstructed images, because fewer low-energy x-ray photons contribute to the image. Images courtesy of National Institutes of Health (NIH), Bethesda, MD, USA.

Fig. 28

Schematic illustration of charge sharing at pixel boundaries and energy loss due $K$-escape, which lead to double counting of x-ray pulses at wrong energies and reduction of spectral separation. $E_{\text{fluoro}}$ is the $K$-shell fluorescence x-ray energy.

Fig. 29

Lung images of a 74-year-old woman with breast cancer and signs of fibrosis after radiation therapy, acquired with a single-source CT prototype with photon-counting detector. Data acquisition: $120\times 0.2\mathrm{mm}$ collimation, 0.3 s rotation time, ${\mathrm{CTDI}}_{\mathrm{vol}}=3.89\mathrm{mGy}$, $\mathrm{DLP}=126\mathrm{mGycm}$. Image reconstruction: sharp convolution kernel, $1024\times 1024$ image matrix, 0.4 mm slice width. Excellent visualization of fibrosis and fine details such as fissures is achieved. Images courtesy of Dr. J. Ferda, Pilsen University, Czech Republic.

Fig. 30

(a) Bones of the middle ear—the stapes (yellow circle) has a size of about $2\mathrm{mm}\times 3\mathrm{mm}$. Specimen image acquired with (b) a state-of-the-art medical CT and (c) a single-source CT prototype with photon-counting detector. Data acquisition: $120\times 0.2\mathrm{mm}$ collimation. Spatial resolution is significantly improved. Images courtesy of Dr. A. Persson, Center for Medical Image Science and Visualization (CMIV), Linköping, Sweden.

Fig. 31

Simultaneous imaging of three different contrast agents (iodine, gadolinium, and bismuth) by multi-material decomposition in a dog model. Scan data were acquired with the preclinical photon counting CT prototype and read-out in four energy bins (25 to 50, 50 to 75, 75 to 90, and 90 to 140 keV). Bismuth was administered more than one day prior to scanning. Intravenous administration of gadolinium-based contrast agent was followed by intravenous administration of iodine-based contrast agent after 3 min to simultaneously visualize different phases of renal enhancement. (a) Image acquired at 30 s after start of gadolinium injection at the peak of gadolinium enhancement in the renal cortex. (b) Image acquired at 220 s at the peak of iodine enhancement in the renal cortex. (c) Enhancement curves of gadolinium and iodine in the aorta, renal cortex, medulla, and pelvis. Courtesy of R. Symons, NIH, Bethesda, MD, USA (see also Ref. 49).